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AI/ML Engineer with 3+ years of experience building production machine learning and generative AI systems across financial services and enterprise data platforms. Experienced in designing LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and scalable ML infrastructure. Skilled in transformer-based NLP, vector retrieval systems, distributed data pipelines, and MLOps deployment practices using Python, PyTorch, and TensorFlow. Hands-on with LangChain/LangGraph/LlamaIndex, hybrid retrieval, vector databases (FAISS/Pinecone), and containerized inference using Docker, Kubernetes, and cloud platforms.…AI/ML Engineer with 3+ years of experience building production machine learning and generative AI systems across financial services and enterprise data platforms. Experienced in designing LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and scalable ML infrastructure. Skilled in transformer-based NLP, vector retrieval systems, distributed data pipelines, and MLOps deployment practices using Python, PyTorch, and TensorFlow. Hands-on with LangChain/LangGraph/LlamaIndex, hybrid retrieval, vector databases (FAISS/Pinecone), and containerized inference using Docker, Kubernetes, and cloud platforms.

Jhanavi Putcha

AI Engineer, Data Scientist, Data Engineer, +2

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AI/ML Engineer with 3+ years of experience building production machine learning and generative AI systems across financial services and enterprise data platforms. Experienced in designing LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and scalable ML infrastructure. Skilled in transformer-based NLP, vector retrieval systems, distributed data pipelines, and MLOps deployment practices using Python, PyTorch, and TensorFlow. Hands-on with LangChain/LangGraph/LlamaIndex, hybrid retrieval, vector databases (FAISS/Pinecone), and containerized inference using Docker, Kubernetes, and cloud platforms.…AI/ML Engineer with 3+ years of experience building production machine learning and generative AI systems across financial services and enterprise data platforms. Experienced in designing LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and scalable ML infrastructure. Skilled in transformer-based NLP, vector retrieval systems, distributed data pipelines, and MLOps deployment practices using Python, PyTorch, and TensorFlow. Hands-on with LangChain/LangGraph/LlamaIndex, hybrid retrieval, vector databases (FAISS/Pinecone), and containerized inference using Docker, Kubernetes, and cloud platforms.

Available to hire

AI/ML Engineer with 3+ years of experience building production machine learning and generative AI systems across financial services and enterprise data platforms. Experienced in designing LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and scalable ML infrastructure.

Skilled in transformer-based NLP, vector retrieval systems, distributed data pipelines, and MLOps deployment practices using Python, PyTorch, and TensorFlow. Hands-on with LangChain/LangGraph/LlamaIndex, hybrid retrieval, vector databases (FAISS/Pinecone), and containerized inference using Docker, Kubernetes, and cloud platforms.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at BNY New York, USA

May 1, 2025 - Present

Designed and deployed LLM-powered document intelligence pipelines using LangGraph, LlamaIndex, and GPT-4 to automate regulatory filings and compliance document processing, reducing manual review effort by 41%. Built a RAG platform integrating FAISS and Pinecone with financial data lakes, implementing hybrid vector search and reranking to enable low-latency retrieval for trade and settlement records; improved analyst efficiency by 29%. Fine-tuned transformer-based NLP models (BERT, FinBERT, LLaMA) for named entity recognition and document classification with 95% precision in financial entity extraction. Developed scalable data pipelines using PySpark, Apache Airflow, and Snowflake to process multi-terabyte transaction datasets, enabling near real-time analytics and 2.1× throughput. Trained transformer-based models for financial text classification and sentiment analysis with 26% accuracy improvement. Implemented anomaly detection using XGBoost, autoencoders, and graph-based analytics t

Machine Learning Engineer at LTIMindtree

January 1, 2022 - July 1, 2024

Architected an AI-driven credit risk scoring system using XGBoost, LightGBM, and logistic regression on 12M+ loan records, improving default prediction accuracy from 74% to 91% and enabling faster underwriting decisions. Engineered real-time fraud detection using gradient boosting, isolation forest, and graph-based anomaly detection on card transaction streams, reducing fraud losses by 38% while keeping false positives below 3%. Implemented MLOps workflows using Azure Machine Learning, MLflow, and Docker to automate model training/versioning/deployment, reducing release cycles from weeks to days while ensuring regulatory compliance. Built deep learning models (LSTM and temporal CNN) for account activity forecasting and early fraud/account takeover detection, improving early detection by 27%. Established monitoring dashboards in Power BI integrated with Azure ML APIs to track data drift and performance, enabling proactive retraining and reducing degradation by 30%. Developed transformer